.. figure:: docs/img/logo.png :alt: EnTiSe logo :width: 300px

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A simple tool for generating time series data related to energy systems and building operations.

.. contents:: :depth: 1 :local: :backlinks: top

Introduction

EnTiSe (En-ergy Ti-me Se-ries) is a Python package designed to generate realistic time series data for various energy systems and building operations. It provides a flexible, pipeline- and strategy-based approach to create time series for different applications including HVAC, domestic hot water, electricity, mobility, and occupancy patterns.

Key Features

• Multiple time series types including DHW, HVAC, PV, and more in the works.
• Flexible pipeline- and strategy-based architecture for customizable time series generation.
• Support for dependent methods to create related time series.
• Two access patterns: batch processing via TimeSeriesGenerator or direct method imports for individual control.

Getting Started

To get started, follow these steps:

Requirements

• Python <https://www.python.org/>_ (3.10 or newer)
• Git <https://git-scm.com/>_ for version control (optional, only for development)

Installation

EnTiSe can be installed directly from PyPI:

.. code-block:: bash

pip install entise

For faster installation, you can use uv <https://github.com/astral-sh/uv>_:

.. code-block:: bash

pip install uv uv pip install entise

For development or the latest features, you can install directly from the repository:

.. code-block:: bash

pip install git+https://github.com/tum-ens/entise.git

Or clone the repository and install in development mode:

.. code-block:: bash

git clone https://github.com/tum-ens/need/entise.git cd entise pip install -e ".[dev]"

Now you're ready to use EnTiSe! Check the examples directory for usage examples.

Repository Structure

• entise/: Main project code organized in a Python package.

  • constants/: Definitions of time series types and other constants.
  • core/: Core functionality and base classes.
  • data/: Data files and data handling utilities.
  • methods/: Implementation of various time series generation methods.
  • services/: Auxiliary services supporting the core functionality.

• tests/: Folder for tests; structured by functionality.

• docs/: Documentation source files.

• examples/: Example scripts demonstrating usage of the package.

Usage Guidelines

Basic Usage

EnTiSe provides two flexible ways to generate time series data:

Batch Processing

For generating multiple time series at once, you can use the `Generator` and either add objects one by one or in bulk using DataFrames or dictionaries:

.. code-block:: python

   from entise import Generator

   # Initialize the generator
   gen = Generator()

   # Add objects (e.g., buildings)
   gen.add_objects({
       "id": "building1",
       "hvac": "1R1C",
       "resistance": 2.0,
       "capacitance": 1e5,
       "temp_min": 20.0,
       "temp_max": 24.0,
   })

   # Prepare input data (e.g., weather)
   data = {
       "weather": pd.DataFrame({
           "temp_out": [0.0] * 24,
       }, index=pd.date_range("2025-01-01", periods=24, freq="h"))
   }

   # Generate time series
   summary, df = gen.generate(data)

Direct Method Access

For more granular access you can work with the methods directly:

.. code-block:: python

from entise.methods.pv import PVLib

Create an instance

pvlib = PVLib()

Generate time series

result = pvlib.generate( latitude=48.1, longitude=11.6, power=5000, weather=weather_df )

Access results

summary = result["summary"] timeseries = result["timeseries"]

For more detailed examples, check the examples directory.

Supported or Planned Time Series Types

EnTiSe supports generating time series for the following types:

Integrated:

• Electricity demand
• Domestic Hot Water (DHW)
• Heat pump (COP)
• HVAC (Heating, Ventilation, and Air Conditioning)
• Occupancy data
• Solar Photovoltaic (PV)
• Wind energy

Planned:

• Concentrated Solar Power (CSP) (https://oemof-thermal.readthedocs.io/en/latest/concentrating_solar_power.html)
• Geothermal energy
• Hydroelectric power (https://github.com/IdahoLabResearch/HydroGenerate or https://github.com/9505-PNNL/wmpy_power or https://www.theoj.org/joss-papers/joss.03294/10.21105.joss.03294.pdf)
• Mobility (https://emobpy.readthedocs.io/en/latest/)
• Solar thermal (https://oemof-thermal.readthedocs.io or https://www.theoj.org/joss-papers/joss.03294/10.21105.joss.03294.pdf)
• Tidal energy
• Wave energy

Documentation

Please see the documentation <https://entise.readthedocs.io>_ for further information.

Contribution and Code Quality

We are happy if you want to contribute to this repository! Have a look at the guides in our docs for more information on how to contribute and our coding standards.

Coding Standards

This repository follows consistent coding styles. Refer to CONTRIBUTING.md <./docs/guides/CONTRIBUTING.md>_ for detailed standards.

Changelog

The changelog is maintained in the CHANGELOG.md <CHANGELOG.md>_ file. It lists all changes made to the repository. Follow instructions there to document any updates.

License and Citation

| The code of this repository is licensed under the MIT License (MIT). | See LICENSE <LICENSE>_ for rights and obligations. | Copyright: EnTiSe <https://gitlab.lrz.de/tum-ens/need/entise>_ (c) TU Munich - ENS <https://www.epe.ed.tum.de/en/ens/homepage/>_ | MIT <LICENSE>_

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